NOT APPLICABLE.
NOT APPLICABLE.
This invention relates generally to dynamic imaging, for example, using magnetic resonance (MR) techniques, and more particularly the invention relates to a fast method of image reconstruction using a generalized series for recovering unmeasured data in image reconstruction to enable high-speed imaging. The invention finds particular applications in imaging of time-varying object functions, for example, following wash-in/wash-out process of injected contrast agents, monitoring perfusion/diffusion changes in brain functional studies, and measuring relaxation times in multiple T1-weighted or T2-weighted experiments. However, the invention is also applicable to other imaging modalities including X-ray CT (computerized tomography), PET (positron emission tomography), and the like.
Many MR imaging applications require collecting a time series of images. Conventionally, these images are acquired independently, and as a result often compromise spatial resolution for high temporal resolution or short imaging time. To overcome this problem, several data-sharing methods have been proposed to improve imaging efficiency and speed. One such method relies on data sharing from one temporal frame to another, such as reduced encoded imaging by generalized series reconstruction or RIGR. A unique feature of this method is that one or more high resolution reference images and a sequence of reduced dynamic data sets (usually in central k-space) are collected. Assuming that N Fourier encodings are collected for the reference data set(s) and M encodings are collected for each of the dynamic data sets, a factor N/M improvement in temporal resolution is gained with this data acquisition scheme as compared to the convention full scan imaging method.
One known method, referred to as keyhole, fills in unmeasured high frequency data simply with the data from the data reference set or sets. The RIGR method recovers unmeasured data using the generalized series (GS) model, of which the basis functions are constructed based on the reference image(s). A key step in GS model-base image reconstruction is the determination of the series coefficients, which involve solving a set of linear equations. Although the computational problem is manageable for k-space data truncated only along one direction, it can present a significant problem for data sets truncated in multiple directions as often encountered in multidimensional imaging. To address this problem, a fast approximate solution has been proposed for application in 3D time-resolve angiography. See Madore and Pelc, xe2x80x9cA new way to perform 3D time-resolve angiography,xe2x80x9d Proceedings of the Eighth Annual Meeting of ISMRM, Denver, 2000; p. 697.
The present invention provides a fast algorithm for reconstructing GS model-based images. The algorithm is stable, data consistent, and capable of capturing large signal variations with respect to the reference image(s).
In accordance with the RIGR imaging scheme, one or more high-resolution reference images and a sequence of reduced dynamic data sets are collected.
In accordance with the basic generalized series (GS) model, a dynamic image I(x) is expressed as a product of a weighting function (determined from one or more high resolution reference images) and a local contrast modulation function, which is updated from one image frame to another. The contrast modulation function includes series coefficients which are determined by solving a set of linear equations.
In accordance with the invention, a fast algorithm finds an approximate solution for the sets of linear equations which are then used in GS model-based image reconstruction.
In accordance with another feature of the invention, the weighting function of the GS model is determined from the magnitude of the reference image(s), and a regularization constant is added to the weighting function to provide stability but without adversely affecting accuracy.
In accordance with yet another feature of the invention, the contrast modulation function is determined from the filtered Fourier transform of the dynamic data sets and synthetic Fourier data of the weighting function.
In accordance with yet another feature of the invention, the phase information of the reference images and measured dynamic data are utilized in every time frame along with the estimated data to thereby provide consistency of the reconstructed image.
The invention and objects and features thereof will be more readily apparent from the following detailed description and appended claims: when taken with the drawings.